id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_ae7j2j76jbaubdvmkzdpm2hype	Carlos M. Fernandes	Steady state particle swarm	2019	30	.pdf	application/pdf	13429	2503	77	the particle swarm optimization (PSO) algorithm. strategy for PSO algorithms is proposed: only the least fit particle and its neighbors Keywords Bak–Sneppen model, Particle swarm optimization, Velocity update strategy sophisticated variants of the algorithm, such as PSOs with time-varying parameters state-of-the art PSOs. Furthermore, the size of the test set is small and does not comprise analysis of the performance, comparing the algorithm with standard PSOs and variations asynchronous and steady state update strategy for PSO in which only the least fit particle Algorithm 1 Steady state particle swarm optimization. Table 7 SS-PSOMoore results: solutions quality, convergence speed and success rates. The preceding tests show that the steady state update strategy when implemented in a PSO times of 49,000 functions evaluations (median values over 10 runs for each algorithm). replaced by random values, in SS-PSO the worst particle and its neighbors are updated and GREEN-PSO: conserving function evaluations in particle swarm optimization.	./cache/work_ae7j2j76jbaubdvmkzdpm2hype.pdf	./txt/work_ae7j2j76jbaubdvmkzdpm2hype.txt